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Traditional clustering has focused on creating a single good clustering solution, while modern, high dimensional data can often be interpreted, and hence clustered, in different ways. Alternative clustering aims at creating multiple clustering solutions that are both of high quality and distinctive from each other. Methods for alternative clustering can be divided into objective-function-oriented...
Bhattacharrya distance (BD) is a widely used distance in statistics to compare probability density functions (PDFs). It has shown strong statistical properties (in terms of Bayes error) and it relates to Fisher information. It has also practical advantages, since it strongly relates on measuring the overlap of the supports of the PDFs. Unfortunately, even with common parametric models on PDFs, few...
In this paper, we present a scalable evolutionary algorithm for clustering large and dynamic data sets, called Scalable Evolutionary Clustering with Self Adaptive Genetic Operators (Scalable ECSAGO). The proposed evolutionary clustering algorithm can adapt its genetic operators rate while the evolution leads to the optimal centers of the clusters. The sizes of the clusters are estimated using a hybrid...
While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field is dealing with high dimensional datasets. The class of clustering algorithms that utilises information from Principal Component Analysis has proven very successful in such datasets...
Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not many studies have been dealing with the integration of spatial data mining and temporal data mining...
Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead...
Mean shift spectral clustering (MSSC) brings us an alternative for image segmentation. However, owing to being based on the classical Parzen window estimator (PW) and employing the full data sample for density estimation, the usefulness of MSSC is weakened. In this paper, the improved mean shift spectral clustering (IMSSC) algorithm is proposed by replacing PW with the reduced set density estimator...
Image segmentation algorithm based on fuzzy c-means clustering is an important algorithm in the image segmentation field. It has been used widely. However, it is not successfully to segment the noise image because the algorithm disregards of special constraint information. It only considers the gray information. Therefore, we proposed a weighed FCM algorithm based on Gaussian kernel function for image...
Source IP addresses are often used as a major feature for user modeling in computer networks. Particularly in the field of distributed denial of service (DDoS) attack detection and mitigation traffic models make extensive use of source IP addresses for detecting anomalies. Typically the real IP address distribution is strongly undersampled due to a small amount of observations. Density estimation...
Data clustering constitutes at present a commonly used technique for extracting fuzzy system rules from experimental data. Detailed studies in the field have shown that using above-mentioned method results in significantly reduced structure of fuzzy identification system, maintaining at the same time its high modelling efficiency. In this paper a clustering algorithm, based on a kernel density gradient...
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